The demand for civil navigation systems in harsh environments has been growing over the last several years. The Global Positioning System (GPS) has been the backbone of most current navigation systems, but its usefulness in downtown urban environments or heavily treed terrain is limited due to signal blockages and other signal propagation impairments. To help bridge these signal gaps inertial navigation systems (INS) have been used. For civil applications, INS typically use Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Units (IMU) due to cost, size and regulatory restrictions of higher grade inertial units. An integrated INS/GPS system can provide a continuous navigation solution regardless of the environment.
The integration filters, such as Kalman Filters, are typically used for combining GPS and INS measurements. The Kalman Filter is a minimum mean squared error estimation tool that is the standard for multi-sensor integration. Kalman Filter, regardless of its exact implementation, is generally considered optimal if certain a priori error statistics are given to the algorithm. These parameters are typically developed by the manufacturer or designer of the sensors, but these values are often very general, especially for low cost MEMS IMUs. In such cases, it is often too costly for either the manufacturer or designer to fine tune individual sensors, and thus less than optimal filter tuning parameters are used. Accordingly, there is a need for an efficient and cost-effective mechanism for tuning integration filters for INS/GPS systems.